Papers Multi-Task Learning
“Multi-Task Learning” 태그가 달린 논문 4,109편 · 필터 해제
Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
Model merging combines the parameters of multiple neural networks into a single model without additional training. As fine-tuned large language models (LLMs) proliferate, merging offers a computationally efficient altern…
Multi-Task LearningFederated LearningFeed m Birds with One Scone: Accelerating Multi-task Gradient Balancing via Bi-level Optimization
In machine learning, the goal of multi-task learning (MTL) is to optimize multiple objectives together. Recent works, for example, Multiple Gradient Descent Algorithm (MGDA) and its variants, show promising results with …
Multi-Task LearningReward-Conditioned Reinforcement Learning
Single-task RL agents are typically trained under a fixed reward function, which limits their robustness to reward misspecification and their ability to adapt to changing preferences. We introduce Reward-Conditioned Rein…
Reinforcement LearningMulti-Task LearningPCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting
As advancements in technologies like Internet of Things (IoT), Automatic Speech Recognition (ASR), Speaker Verification (SV), and Text-to-Speech (TTS) lead to increased usage of intelligent voice assistants, the demand f…
Multi-class ClassificationSpeaker VerificationMulti-Task LearningSpeech RecognitionBD-Merging: Bias-Aware Dynamic Model Merging with Evidence-Guided Contrastive Learning
Model Merging (MM) has emerged as a scalable paradigm for multi-task learning (MTL), enabling multiple task-specific models to be integrated without revisiting the original training data. Despite recent progress, the rel…
Contrastive LearningMulti-Task LearningCrab$^{+}$: A Scalable and Unified Audio-Visual Scene Understanding Model with Explicit Cooperation
Developing Audio-Visual Large Language Models (AV-LLMs) for unified scene understanding is pivotal in multimodal intelligence. While instruction tuning enables pre-trained models with multi-task abilities, we observe tha…
Multi-Task LearningScene UnderstandingRoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots
Recent advances in robot learning have accelerated progress toward generalist robots that can perform everyday tasks in human environments. Yet it remains difficult to gauge how close we are to this vision. The field lac…
Multi-Task LearningTrade-offs in Ensembling, Merging and Routing Among Parameter-Efficient Experts
While large language models (LLMs) fine-tuned with lightweight adapters achieve strong performance across diverse tasks, their performance on individual tasks depends on the fine-tuning strategy. Fusing independently tra…
Multi-Task LearningAn Analysis of Multi-Task Architectures for the Hierarchic Multi-Label Problem of Vehicle Model and Make Classification
Most information in our world is organized hierarchically; however, many Deep Learning approaches do not leverage this semantically rich structure. Research suggests that human learning benefits from exploiting the hiera…
Hierarchical Multi-label ClassificationMulti-Task LearningCan Computational Reducibility Lead to Transferable Models for Graph Combinatorial Optimization?
A key challenge in deriving unified neural solvers for combinatorial optimization (CO) is efficient generalization of models between a given set of tasks to new tasks not used during the initial training process. To addr…
Multi-Task LearningMulti-Level Bidirectional Decoder Interaction for Uncertainty-Aware Breast Ultrasound Analysis
Breast ultrasound interpretation requires simultaneous lesion segmentation and tissue classification. However, conventional multi-task learning approaches suffer from task interference and rigid coordination strategies t…
Lesion SegmentationMulti-Task LearningAssessing the Impact of Speaker Identity in Speech Spoofing Detection
Spoofing detection systems are typically trained using diverse recordings from multiple speakers, often assuming that the resulting embeddings are independent of speaker identity. However, this assumption remains unverif…
Speaker RecognitionMulti-Task LearningClapeyron Neural Networks for Single-Species Vapor-Liquid Equilibria
Machine learning (ML) approaches have shown promising results for predicting molecular properties relevant for chemical process design. However, they are often limited by scarce experimental property data and lack thermo…
Multi-Task LearningEnsemble Prediction of Task Affinity for Efficient Multi-Task Learning
A fundamental problem in multi-task learning (MTL) is identifying groups of tasks that should be learned together. Since training MTL models for all possible combinations of tasks is prohibitively expensive for large tas…
Multi-Task LearningDeepMTL2R: A Library for Deep Multi-task Learning to Rank
This paper presents DeepMTL2R, an open-source deep learning framework for Multi-task Learning to Rank (MTL2R), where multiple relevance criteria must be optimized simultaneously. DeepMTL2R integrates heterogeneous releva…
Multi-Task LearningRobust multi-task boosting using clustering and local ensembling
Multi-Task Learning (MTL) aims to boost predictive performance by sharing information across related tasks, yet conventional methods often suffer from negative transfer when unrelated or noisy tasks are forced to share r…
Multi-Task LearningMulti-Task Learning with Additive U-Net for Image Denoising and Classification
We investigate additive skip fusion in U-Net architectures for image denoising and denoising-centric multi-task learning (MTL). By replacing concatenative skips with gated additive fusion, the proposed Additive U-Net (Ad…
Multi-Task LearningImage DenoisingLearning Perceptual Representations for Gaming NR-VQA with Multi-Task FR Signals
No-reference video quality assessment (NR-VQA) for gaming videos is challenging due to limited human-rated datasets and unique content characteristics including fast motion, stylized graphics, and compression artifacts. …
Video Quality AssessmentMulti-Task LearningModular Multi-Task Learning for Chemical Reaction Prediction
Adapting large language models (LLMs) trained on broad organic chemistry to smaller, domain-specific reaction datasets is a key challenge in chemical and pharmaceutical R&D. Effective specialisation requires learning new…
parameter-efficient fine-tuningChemical Reaction PredictionForward reaction predictionMulti-Task LearningTowards Long-Lived Robots: Continual Learning VLA Models via Reinforcement Fine-Tuning
Pretrained on large-scale and diverse datasets, VLA models demonstrate strong generalization and adaptability as general-purpose robotic policies. However, Supervised Fine-Tuning (SFT), which serves as the primary mechan…
Reinforcement LearningMulti-Task LearningContinual Learning